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基于深度学习的冬季小麦存储的自动分级评估.

Hecang Zang1,2, Xinqi Su1,3, Yanjing Wang4

  • 1Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou, China.

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概括

一个新的深度学习模型,MLP_U-Net,使用无人机图像准确地评分冬季小麦的住宿. 这项技术提供了一种可靠和有效的方法来评估作物损害,并帮助农业保险.

关键词:
无人机图像 无人机图像深度学习是一种深度学习.寄宿区的寄宿区是一个寄宿区.住宿级别:住宿级别为住宿级别.冬季小麦 冬季小麦

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科学领域:

  • 农业工程 农业工程
  • 遥感 遥感 遥感 遥感
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 冬季小麦的存放显著影响作物产量和质量,需要对农业保险和种子选择进行准确的评估.
  • 评估住宿的传统方法是劳动密集的,主观的,缺乏可靠性,阻碍了有效的损失评估.

研究的目的:

  • 开发和验证一种新的深度学习模型,用于准确和自动分类冬季小麦存储.
  • 用无人机 (UAV) 遥感数据量化评估降落程度和区域.

主要方法:

  • 一个分类语义细分的多任务神经网络,MLP_U-Net,是基于U-Net架构设计的,并改进了MLP模块.
  • 该模型采用了一种通用编码器来保证强度,并利用无人机在不同高度捕捉到的冬季小麦存放图像.
  • 数据集是从82个冬季小麦品种的图像中创建的,可以对冬季小麦品种的严重程度和范围进行细分和分类.

主要成果:

  • MLP_U-Net表现出卓越的性能,在飞行高度为30米的无人机飞行高度下,在分类冬季小麦安置度 (96.1%) 和安置面积 (92.2%) 中实现了高准确度.
  • 在50米的无人机飞行高度,该模型仍然提供了准确的分级,准确度为84.1%的住宿度和84.7%的住宿面积.
  • 该模型被证明是强大而高效的,特别是在小样本数据集中,超过了传统方法.

结论:

  • MLP_U-Net模型提供了一个高度准确,强大和高效的解决方案,用于使用无人机遥感器对冬季小麦的分类.
  • 这项技术为评估冬季小麦灾难严重程度和农业损失,改善保险索赔处理和种子选择提供了宝贵的技术参考.